Analysis of Nonlinear Activation Functions for Classification Tasks Using Convolutional Neural Networks

Abstract

We give an overview of several nonlinear activation functions in deep neural networks that are used to solve various complex machine learning applications. We make the comparison on effectiveness of using these functions and conduct empirical analysis on the Cat/Dog dataset with the aim of clarifying which function produce the best results overall. We also make the prediction by taking the number of input images on CNN model by applying different activation functions at the number of hidden layers.